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    Determination of cutting parameters for silicon wafer with a Diamond Wire Saw using an artificial neural network
    (Pergamon-Elsevier Science Ltd, 2017) Kayabasi, Erhan; Ozturk, Savas; Celik, Erdal; Kurt, Huseyin
    An Artificial Neural Network (ANN) simulation was utilized to predict surface roughness values (R-a) for a Silicon (Si) ingot cutting operation with a Diamond Wire Saw (DWS) cutting machine. Experiments were done on a DWS cutting machine to obtain data for training, testing and validation of the ANN. The DWS cutting operation had three parameters affecting surface quality: spool speed, z axis speed and oil ratio in a coolant slurry. Other parameters such as wire tension, wire thickness, and work piece diameter were assumed as constant. The DWS cutting machine performed 28 cutting operations with different values of the selected three parameters and new cutting parameters were derived for different cutting conditions to achieve the best surface quality by using the ANN. Wafers 400 mu m thick were cut from a n-type single crystalline Si ingot in a STX 1202 DWS cutting machine. R-a values were measured three times from different regions of the wafers. In ANN simulation 70% of R-a values were used as training, 15% of R-a values were used as validation and 15% of R-a values were used to test data in ANN. The ANN simulation results validated training output data with success above 99%. Consequently, the R-a values corresponding to the cutting parameters, and also proper cutting parameters for specific R-a values were determined for DWS cutting using the ANN. (C) 2017 Elsevier Ltd. All rights reserved.
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    Determination of lapping parameters for silicon wafer using an artificial neural network
    (Springer, 2018) Ozturk, Savas; Kayabasi, Erhan; Celik, Erdal; Kurt, Huseyin
    An artificial neural network (ANN) simulation was utilized to determine the lapping parameters such as rotation speed, lapping duration and lapping pressure under a constant slurry supply for n-type crystalline Silicon (c-Si) wafers. Experiments were done with a Logitech PM5 lapping and polishing machine to obtain input data and target data for training, testing and validation of ANN. Lapping operation had five main parameters affecting surface quality: rotation speed, lapping duration, lapping pressure, flowrate of abrasive slurry and particle size in abrasive slurry. However, in this study slurry flowrate was assumed constant due the researches performed before. 218 lapping operations were performed with different values of the selected parameters and new lapping parameters were derived for different lapping conditions to achieve the best surface quality by using an ANN. In this study, wafers in 400 A mu m thickness cut under identical conditions from n-type single c-Si ingot in a STX 1202 DWS cutting machine were employed. Surface roughness (R (a) ) values were measured three times from different points of the wafers after lapping with a contact type surface roughness measurement tool using a microscopic scale stylus profiler (SP). In ANN simulation 70% of R (a) values were utilized for training, 15% of R (a) values were utilized for validation and 15% of R (a) values were utilized for test data. Results obtained from ANN simulation validated with a success above 99%.
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    Prediction of nano etching parameters of silicon wafer for a better energy absorption with the aid of an artificial neural network
    (Elsevier Science Bv, 2018) Kayabasi, Han; Ozturk, Savas; Celik, Erdal; Kurt, Huseyin; Arcaldioglu, Erol
    To enhance energy absorption of photovoltaics, several etching experiments with various parameters were performed. In addition, an Artificial Neural Network (ANN) simulation was utilized to predict chemical nano etching parameters such as masking and etching durations for Silicon (Si) solar cell applications to reach minimum surface reflectance in an optimum etching duration. Experiments were performed with different masking and etching durations to determine the characteristics of surface reflectance of micro textured n-type single crystalline Si wafers in 25mmx25mm width and 300 gm thickness to provide training data for ANN. For this purpose, solutions with identic properties including Ag nanoparticles were applied with different application durations on the surfaces of n-type single crystalline Si wafers to cover partially the Si surfaces with Ag nano-particles at masking step. After, partially masked Si surfaces were exposed to chemical nano etching to develop nano-sized porous structures under different etching durations in an identic acidic etching solution. For the etching of Si wafers, 32 masking and etching processes were performed. Reflectance measurements and SEM images were evaluated to determine the optimum etching duration resulting the best surface quality with minimum reflectance. In addition, reflectance values were utilized as input data for training, testing and validation steps of developed ANN. In the ANN simulation, 70% of reflectance values were used as training, 15% of reflectance values were used as validation and 15% of reflectance values were used to test data in the ANN. Consequently, surface reflectance values under different masking and etching durations were predicted with the new parameter set by using the trained ANN with a success level above 99%.